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1دورية أكاديمية
المؤلفون: Robert Ramírez-Hernández, Dámaso Cáceres-Govea
المصدر: Minería y Geología, Vol 33, Iss 3, Pp 279-292 (2017)
مصطلحات موضوعية: redes de drenaje, río San Cristóbal, fractales, autoafinidad, lagunaridad., Mining engineering. Metallurgy, TN1-997, Geology, QE1-996.5, Mineralogy, QE351-399.2
وصف الملف: electronic resource
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2دورية أكاديمية
المؤلفون: Ramírez-Hernández, Robert, Cáceres-Govea, Dámaso
المصدر: Minería & Geología; Vol. 33, Núm. 3 (2017): Julio-Septiembre; 279-292 ; 1993-8012
مصطلحات موضوعية: geology, river of San Cristóbal, fractal, lacunarity, hurst, self-affine, geología, redes de drenaje, río San Cristóbal, fractales, autoafinidad, lagunaridad
وصف الملف: application/pdf
العلاقة: http://revista.ismm.edu.cu/index.php/revistamg/article/view/art3_No3_2017/803Test; http://revista.ismm.edu.cu/index.php/revistamg/article/view/art3_No3_2017Test; http://ninive.ismm.edu.cu/handle/123456789/652Test
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3
المؤلفون: Leal Freitez, Jorge Alberto
المساهمون: Ochoa Gutiérrez, Luis Hernán
المصدر: Repositorio UN
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombiaمصطلحات موضوعية: Borehole resistivity imaging, Hydrocarbon reservoirs, Oil and gas wells, Imágenes resistivas de pozo, Dimensión fractal, 550 - Ciencias de la tierra, automatic picking, Aprendizaje automático, Geologic planes, Automatic dip classification, Planos geológicos, Clasificación automática de buzamientos, 500 - Ciencias naturales y matemáticas, Machine learning, Lacunarity, Fractal dimension, 000 - Ciencias de la computación, información y obras generales, Lagunaridad, Yacimientos de hidrocarburos
وصف الملف: 136 páginas; application/pdf
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::9e781b5771a6f3fc8886f2d8af6af3e7Test
https://repositorio.unal.edu.co/handle/unal/80725Test -
4دورية أكاديمية
المؤلفون: Rubén Darío Arizabalo, Klavdia Oleschko, Gabor Korvin, Manuel Lozada, Ricardo Castrejón, Gerardo Ronquillo
المصدر: Geofísica Internacional, Vol 45, Iss 2, Pp 99-113 (2006)
مصطلحات موضوعية: lagunaridad, análisis fractal, autosemejanza, método r/s, escalamiento, registros de pozo, Geophysics. Cosmic physics, QC801-809
وصف الملف: electronic resource
العلاقة: http://revistagi.geofisica.unam.mx/index.php/RGI/article/view/281Test; https://doaj.org/toc/0016-7169Test; https://doaj.org/toc/2954-436XTest
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5رسالة جامعية
المؤلفون: Leal Freitez, Jorge Alberto
المساهمون: Ochoa Gutiérrez, Luis Hernán
مصطلحات موضوعية: 550 - Ciencias de la tierra, 500 - Ciencias naturales y matemáticas, 000 - Ciencias de la computación, información y obras generales, Oil and gas wells, Machine learning, Fractal dimension, Lacunarity, Borehole resistivity imaging, Geologic planes, automatic picking, Automatic dip classification, Hydrocarbon reservoirs, Aprendizaje automático, Dimensión fractal, Lagunaridad, Imágenes resistivas de pozo, Planos geológicos, Clasificación automática de buzamientos, Yacimientos de hidrocarburos
وصف الملف: 136 páginas; application/pdf
العلاقة: Aggarwal, C., 2015, Data mining. The textbook, first edition. Springer, New York, 181-488pp.; Allain, C., Cloitre, M., 1991, Characterizing the lacunarity of random and deterministic fractal sets. Physical Review A, 44(6), 3552 - 3553. https://doi.org/10.1103/PhysRevA.44.3552Test; Alpaydin, E., 2014, Introduction to machine learning, third edition. The MIT Press, Cambridge, 27-238pp.; Al-Sit, W., Al-Nuaimy, W., Marelli, M., Al- Ataby, A., 2015, Visual texture for automated characterization of geological features in borehole televiewer imagery. Journal of Applied Geophysics, 119, 39-146pp. http://dx.doi.org/10.1016/j.jappgeo.2015.05.015Test; Arizabalo, R., Oleschko, K., Gabor, K., Lozada, M., Castrejón, R., Ronquillo, G., 2006, Lacunarity of geophysical well logs in the Cantarell oil field, Gulf of Mexico. Geofísica International, 45(2), 99-105pp.; Assous, S., Elkington, P., Clark, S., Whetton, J., 2013, Automated detection of planar geological feature in borehole image. Society of Exploration Geophysicists, 79 (1). D11-D19pp. https://doi.org/10.1190/geo2013-0189.1Test; Asquith, G., Krygowski, D., 2004, Basic well log analysis, second edition. The American Association of Petroleum Geologist, Tulsa, 31pp.; Arora, N., Sarvani, G., 2017, A review paper on Gabor filter algorithm & its application. IJARECE, 6 (9), 1003-1007pp. doi:10.17148/IJARCCE.2017.6492; Awad, M., Khanna, R., 2015, Efficient learning machines, second edition. Apress Open, Berkeley, 14-17pp.; Ayad, A., Amrani, M., Bakkali, S., 2019, Quantification of the disturbances of phosphate series using the box-counting method on geoelectrical images (Sidi Chennane, Morocco). International Journal of Geophysics, 2019(12), 1-12. https://doi.org/10.1155/2019/2565430Test; Barnsley, M., 1993, Fractals Everywhere, second edition. Morgan Kaufmann, Atlanta, 171pp.; Bloem, P., 2010, Machine learning and fractal geometry. M.Sc. Thesis, University of Amsterdam. iii-8pp.; Boggs, S., 2009, Petrology of sedimentary rocks, second edition. Cambridge University Press, Cambridge, 194-314pp.; Boggs, S., 2014, Principles of sedimentology and stratigraphy, fifth edition. Pearson Educational Limited, Edinburgh, 76-135pp.; Brownlee, J., 2016, What is a Confusion Matrix in Machine Learning. Machine Learning Mastery, 18 November 2016, https://machinelearningmastery.com/confusion-matrix-machine-learningTest/ (accessed 6 June 2020).; Burger, W., Burge, M., 2009, Principles of digital image processing. Fundamental techniques, first edition. Springer, Hagenberg, 57-122pp.; Burger, W., Burge, M., 2009, Principles of digital image processing. Core algorithms, first edition. Springer, Hagenberg, 110pp.; Changchun, Z., Ge, S., 2002, A Hough transform-based method for fast detection of fixed period sinusoidal curves in images. Signal Processing 6th International Conference, 909-912pp. DOI:10.1109/ICOSP.2002.1181204; Cheng, G., Guo, W., 2017, Rock images classification by using deep convolutional neural network. Journal of Physiscs, 887, 1-7pp. DOI:10.1088/1742-6596/887/1/012089; Conway, D., Myles, J., 2012, Machine learning for hackers, first edition. O’Reilly, Sebastopol, 17pp.; Davis, G., Reynolds, S., Kluth, C., 2012, Structural geology of rocks and regions, third edition. John Wiley and Sons, Hoboken, 786pp.; Desarda, A., 2019, Understanding AdaBoost. Towards Data Science, 17 January 2019, https://towardsdatascience.com/understanding-adaboost-2f94f22d5bfeTest (accessed 7 June 2020).; Ellis, D., Singer, J., 2008, Well logging for earth scientists, second edition. Springer, Ridgefield, 20pp.; Geron, A., 2019, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, second edition. O’Reilly Media Inc., Sebastopol, 177pp.; Glander, S., 2018, Machine Learning Basics - Gradient Boosting & XGBoost. Shirin's playgRound, 29 November 2018, https://www.shirin-glander.de/2018/11/ml_basics_gbmTest/ (accessed 8 June 2020).; Han, J., Kamber, M., Pei, J., 2012, Data mining. Concepts and techniques, third edition. Morgan Kaufmann, Waltham, 254-460pp.; Harvey, A., Fotopoulos, G., 2016, Geological mapping using machine learning algorithms. The international Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI (B8), 423-430pp. DOI:10.5194/ISPRS-ARCHIVES-XLI-B8-423-2016; He, C., Wang, W., 2010, A PCNN-Based Edge Detection Algorithm for Rock Fracture Images, 2010 Symposium on Photonics and Optoelectronics, 2010, 1-4pp. 10.1109/SOPO.2010.5504347.; Joseph, R., 2018, Grid Search for model tuning. Towards Data Science, 29 December 2018, https://towardsdatascience.com/grid-search-for-model-tuning-3319b259367eTest (accessed 8 June 2020).; Khan, J., 2019, Guide to image inpainting: using machine learning to edit and correct defects in photos. Medium Heartbeat, 7 August 2019, https://heartbeat.fritz.ai/guide-to-image-inpainting-using-machine-learning-to-edit-and-correct-defects-in-photos-3c1b0e13bbd0Test (accessed 5 June 2019).; Koehrsen, W., 2018, Improving the Random Forest in Python Part 1. Towards Data Science, 6 January 2018. https://towardsdatascience.com/improving-random-forest-in-python-part-1-893916666cdTest (accessed 10 January 2020).; Leal, J., Ochoa, L., Contreras, C., 2018, Automatic identification of calcareous lithologies using support vector machines, borehole logs and fractal dimension of borehole electrical imaging. Earth Sciences Research Journal, 22(2), 75-82pp. https://doi.org/10.15446/esrj.v22n2.68320Test; Leal, J., Ochoa, L., Garcia, G., 2016, Identification of natural fractures using resistive image logs, fractal dimension and support vector machines. Ingeniería e Investigación, 36(3), 125-132pp. https://doi.org/10.15446/ing.investig.v36n3.56198Test; Li, J., Sun, C., Du, Q., 2006, A new box-counting method for estimation of image fractal dimension. International Conference on Image Processing, 2006, 3029-3032. DOI:10.1109/ICIP.2006.313005.; Lisle, R., 2004, Geological structures and maps. A practical guide, third edition. Elsevier, Oxford, 2pp.; Luthi, S., 2001, Geological well logs. Their use in reservoir modeling, first edition. Springer, Berlin, 53pp.; Mandelbrot, B., 1983, The fractal geometry of nature, second edition. W. H. Freeman and Company, New York, 14pp.; Maynberg, O., Kush, G., 2013, Airborne crown density estimation. International Society For Photogrammetry And Remote Sensing, 2 (49), 49-54pp. https://doi.org/10.5194/isprsannals-II-3-W3-49-2013Test; Moreno, G., García, O., 2006, Quantitative characterization of fracture patterns with circular windows and fractal analysis., Geología Colombiana, (31), 73-74pp.; Morton, D., Woods, A., 1992, Development geology reference manual. AAPG Methods in exploration V10., Tulsa, 174pp.; Neer, K., Mathur, S., 2015, An improved method of edge detection based on Gabor wavelet transform. Recent Advances in Electrical Engineering and Electronic Devices, 184-191pp.; Nelson, R., 2001, Geologic analysis of naturally fractured reservoirs, second edition. Gulf Professional Publishing, Woburn, 23pp.; Nichols, G., 2009, Sedimentology and stratigraphy, second edition. Willey-Blackwell, Chichester, 66-88pp.; Ochoa, L., Niño, L., Vargas, C., 2018, Fast estimation of earthquake epicenter distance using a single seismological station with machine learning techniques. DYNA, 85 (204), 161-168pp. https://doi.org/10.15446/dyna.v85n204.68408Test; Oppenheimer, A., 2018, ¡Sálvese quien pueda! EL trabajo del futuro en la era de la automatización, primera edición. Penguin Random House Group Editorial, Ciudad de México, 6pp.; Park, S., Kim, Y., Ryoo, C. Sanderson, D., 2010, Fractal analysis of the evolution of a fracture network in a granite outcrop, SE Korea. Geosciences Journal, 14(1), 201-215pp. https://doi.org/10.1007/s12303-010-0019-zTest; Parker, J., 2011, Algorithms for image processing and computer vision, second edition. John Wiley and Sons, Indianapolis, 85pp.; Plotnick, R., Garner, R., Hargrove, W., Prestegaard, K., Perlmutter, M., 1996, Lacunarity analysis: A general technique for the analysis of spatial patterns. Physical Review E, 53(5461), 5461-5468. https://doi.org/10.1103/PhysRevE.53.5461Test; Pratt, W., 2007, Digital image processing, fourth edition. John Wiley and Sons, Los Altos, 421pp.; Quan, Y., Xu, Y., Sun, Y., Luo, Y., 2014, Lacunarity analysis on image patterns for texture classification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, The United States Of America, 23-28 June. DOI:10.1109/CVPR.2014.28; Quintanilla, C., Cacau, D., Dos Santos, R., Ribeiro, E., Leta, F., Gonzalez, E., 2017, Improving accuracy of automatic fracture detection in borehole images with deep learning and GPUs. 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 345-350pp. DOI:10.1109/SIBGRAPI.2017.52.; Raghupathy, K., 2004, Curve tracing and curve detection in images. M.Sc. Thesis, Cornell University. pp. ii.; Ranjay, K., 2017, Computer vision: Foundation and Applications, first edition. Stanford University, Stanford, 17pp.; Rider, M., 2000, The geological interpretation of well logs, second edition. Rider – French Consulting Ltd., Sutherland, 67pp.; Roy, A., Perfect, E., Dunne, W., Mackay, L., 2007, Fractal characterization of fracture networks. An improved box-counting technique. Journal of Geophysical Research, (112), 1-2pp. https://doi.org/10.1029/2006JB004582Test; Russell, S., Norvig, P., 2010, Artificial intelligence a modern approach, third edition. Prentice Hall, Upper Saddle River, 698-764pp.; Sadeghi, B., Madeni, N., Carranza, E., 2014, Combination of geostatistical simulation and fractal modeling for mineral resource classification. Journal of Geochemical Exploration, 149(10), 59-73pp. http://dx.doi.org/10.1016/j.gexplo.2014.11.007Test; Schlager, W., 2004, Fractal nature of stratigraphic sequences. GeoScience World, 32(3), 185-188pp. https://doi.org/10.1130/G20253.1Test; Schlumberger, 2013, FMI-HD High-definition formation microimager. Schlumberger brochure, 4pp.; Schlumberger, 1999, Geologic Applications of Dipmeter and Borehole Images. Schlumberger Educational Services, 31-322pp.; Schott, M., 2019, Random forest algorithm for machine learning. Medium, 25 April 2019, https://medium.com/capital-one-tech/random-forest-algorithm-for-machine-learning-c4b2c8cc9febTest (accessed 10 April 2020).; Shapiro, L., Stockman, G., 2001, Computer Vision. The University of Washington, 107-332pp.; Singh, H., 2018, Understanding Gradient Boosting Machines. Towards Data Science, 3 November 2018, https://towardsdatascience.com/understanding-gradient-boosting-machines-9be756fe76abTest (accessed 8 June 2020).; Singh, V., 2019, Model-based feature importance. Towards data sciences, 3 January 2019, https://towardsdatascience.com/model-based-feature-importance-d4f6fb2ad403Test (accessed 31 July 2020).; Tan, T., Stainbach M., Kumar, V., 2006, Introduction to data mining, first edition. Pearson Addison-Wesley, Boston, 297-598pp.; Telea, A., 2004, An image inpainting technique based on the fast marching method. Journal of Graphic Tools, 9 (1), 25-36pp. https://doi.org/10.1080/10867651.2004.10487596Test; Turcotte, D., 1997, Fractal and chaos in geology and geophysics, second edition. Cambridge University, Cambridge, 166pp.; Twiss, R., Moores, E., 2006, Structural geology, second edition. W. H. Freeman and Company, New York, 50pp.; Vasiloudis, T., 2019, Block-distributed Gradient Boosted Trees. Theodore Vasiloudis, 26 August 2019, http://tvas.me/articles/2019/08/26/Block-Distributed-Gradient-Boosted-Trees.htmlTest (accessed 15 November 2020).; Vivas, M., 1992, A techniques for inter well description by applying geostatistic and fractal geometry methods to well logs and core data. Doctoral dissertation, University of Oklahoma, 16pp.; Wang, W., Liao, H., Huang, Y., 2007, Rock fractured tracing based on image processing and SVM. Third International Conference of Natural Computation, 1, 632-635pp. 10.1109/ICNC.2007.643; Weatherford, 2014, Compact microimager. Weatherford brochure, 1-4pp.; https://repositorio.unal.edu.co/handle/unal/80725Test; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.coTest/
الإتاحة: https://doi.org/10.1103/PhysRevA.44.3552Test
https://doi.org/10.1016/j.jappgeo.2015.05.015Test
https://doi.org/10.1190/geo2013-0189.1Test
https://doi.org/10.1155/2019/2565430Test
https://doi.org/10.1109/ICOSP.2002.1181204Test
https://doi.org/10.1088/1742-6596/887/1/012089Test
https://doi.org/10.5194/ISPRS-ARCHIVES-XLI-B8-423-2016Test
https://doi.org/10.15446/esrj.v22n2.68320Test
https://doi.org/10.15446/ing.investig.v36n3.56198Test
https://doi.org/10.1109/ICIP.2006.313005Test -
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المصدر: Revista ECIPerú; Vol. 13 Núm. 1 (2016); 7
ECIPERÚ
Centro Nacional de Planeamiento Estratégico
instacron:CEPRECYTمصطلحات موضوعية: Dimensión Fractal, Textura, Succolarity, Copaifera, Succolaridad, Lacunarity, Fractal Dimension, Texture, Lagunaridad
وصف الملف: application/pdf
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=od______3056::27848f039f48d01dcc58958188bf2d1dTest
https://revistas.eciperu.net/index.php/ECIPERU/article/view/75Test -
7دورية أكاديمية
المصدر: Latin-American Journal of Physics Education, ISSN 1870-9095, Vol. 5, Nº. 2, 2011
مصطلحات موضوعية: Atmospheric particles, Hurst coefficient, Lacunarity, Partículas atmosféricas, Coeficiente de Hurst, Lagunaridad
وصف الملف: application/pdf
العلاقة: http://dialnet.unirioja.es/servlet/oaiart?codigo=3696055Test; (Revista) ISSN 1870-9095
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8مورد إلكتروني
المصدر: Latin-American Journal of Physics Education, ISSN 1870-9095, Vol. 5, Nº. 2, 2011
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9مورد إلكتروني
المصدر: Latin-American Journal of Physics Education, ISSN 1870-9095, Vol. 5, Nº. 2, 2011
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10مورد إلكتروني
المصدر: Latin-American Journal of Physics Education, ISSN 1870-9095, Vol. 5, Nº. 2, 2011
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